Modern vehicles need precise information about their navigation state such as position, velocity, attitude, and related dynamical states to perform their functions. For example, a robotic airplane may require such navigation state information to point a camera at a target, navigate to a destination, and otherwise control its movement. Inputs from multiple sensors may be needed to determine such navigation state of the vehicle. Conventional techniques for estimating the navigation state mostly rely on macroscopic moving parts or costly optical structures such as ring-laser and fiber-optic gyros. Lower-cost solid state inertial sensors have also been used but their lower performance requires more complex implementation using feedback signals to stabilize the navigation state information over extended periods of time.
In conventional navigation systems using such solid state inertial sensors, algorithms are complex and may require excessive computational resources to take advantage of the feedback signals. The algorithms are often executed in digital signal processors (DSPs) to process the feedback signals. Such software algorithms, however, are often too complex or require excessive computational resources for execution in resource-constrained embedded DSPs.
Further, feedback signals from solid state inertial sensors are prone to errors. Consequently, the navigation state of the vehicle estimated from the feedback signals may become corrupted by the very signals that are intended to provide stabilization to the navigation state information. Conventional methods to guard against sensor errors are inflexible and may not be adjusted according to various vehicle configurations and dynamics.